Abstract Ọnụ ọgụgụ dị elu nke mmepụta ike fotovoltaic ga-enwe mmetụta ọjọọ na nkwụsi ike nke usoro ike, na nchekwa ike na-ewere dị ka otu n’ime ụzọ dị irè iji kpochapụ mmetụta ndị a. Akwụkwọ a na-enyocha mmetụta nke mmepụta ike fotovoltaic na usoro ike site n’echiche nke ike na-agba ọsọ, wee nyochaa mmetụta nke nchekwa ike na-egbochi mmetụta ahụ. Nke mbu, ewebata ụdị nkesa nke puru omume na ụdị nchekwa ike nke akụrụngwa na sistemu ike, ewebatakwa usoro nlele Latin hypercube na usoro gram-Schmidt normalization. Nke abuo, e guzobe otutu ihe nleba anya ihe nleba anya, nke na-atụle ego nke usoro nchekwa nchekwa ike, ihe puru omume nke ike nke alaka alaka na nkwụsị netwọk nke grid ike. Ngwọta kachasị mma nke ọrụ ebumnobi bụ nke sitere na mkpụrụ ndụ ihe nketa algọridim. N’ikpeazụ, a na-eme ihe ngosi ahụ na IEEE24 node test system iji nyochaa mmetụta nke ikike ịnweta fotovoltaic dị iche iche na ịnweta ebe dị na usoro ike na mmetụta nke nchekwa ike na usoro ike, na nhazi nchekwa nchekwa kachasị mma kwekọrọ na ikike fotovoltaic dị iche iche. enwetara.

Key words photovoltaic power generation; Energy storage system; Optimized configuration; Probability power flow; Genetic algorithm (ga)

Igwe ọkụ fotovoltaic nwere uru nke nchebe gburugburu ebe obibi ndụ ndụ ndụ na imeghari, a na-ewerekwa ya dị ka otu n’ime ike kachasị ike. Ka ọ na-erule 2020, ikike nchikota nke China nke arụnyere ike nke fotovoltaic eruola 253 nde kw. Nkwụsịtụ na ejighị n’aka nke nnukwu ike PV na-emetụta usoro ike, gụnyere okwu nke ịkpụcha elu, nkwụsi ike na ịtụfu ọkụ, na grid kwesịrị ịmalite usoro ndị ọzọ na-agbanwe agbanwe iji nagide okwu ndị a. A na-ewere nchekwa ike dị ka ụzọ dị irè isi dozie nsogbu ndị a. Ngwa nke usoro nchekwa ike na-eweta ihe ngwọta ọhụrụ maka nnukwu njikọ fotovoltaic grid.

At present, there are many researches on photovoltaic power generation, energy storage system and probability power flow at home and abroad. A large number of literature studies show that energy storage can improve the utilization rate of photovoltaic and solve the stability of photovoltaic grid connection. In the configuration of energy storage system in new energy power station, attention should be paid not only to the control strategy of optical storage and wind storage, but also to the economy of energy storage system. In addition, for the optimization of multiple energy storage power stations in the power system, it is necessary to study the economic model of the operation of energy storage power stations, the site selection of the starting point and end point of photovoltaic transmission channels and the site selection of energy storage. However, the existing research on optimal configuration of energy storage system does not consider the specific impact on power system, and the research on multi-point system does not involve large-scale optical storage operation characteristics.

Site na mmepe dị ukwuu nke mmepụta ike ọhụrụ a na-ejighị n’aka dị ka ike ikuku na fotovoltaic, ọ dị mkpa iji gbakọọ ike nke usoro ike na nhazi nhazi nke usoro ike. Dịka ọmụmaatụ, akwụkwọ ndị ahụ na-amụ ebe kachasị mma na oke ike nke nchekwa ike na usoro ike na ike ikuku. Na mgbakwunye, mmekọrịta dị n’etiti ọtụtụ isi mmalite ike ọhụrụ kwesịkwara ịtụle na mgbako nke ike na-asọ. Otú ọ dị, ọmụmụ ihe niile dị n’elu dabeere na deterministic usoro ịgba ọsọ ike, nke na-adịghị atụle ejighị n’aka nke ike ọhụrụ ike. Akwụkwọ ndị ahụ na-atụle ejighị n’aka nke ike ikuku ma na-etinye usoro nke ike na-eme ka ọ dịkwuo mma iji bulie saịtị nhọrọ nke usoro nchekwa nchekwa ike, nke na-eme ka akụ na ụba na-arụ ọrụ dịkwuo mma.

At present, different probabilistic power flow algorithms have been proposed by scholars, and data mining methods of nonlinear probabilistic power flow based on Monte Carlo simulation method have been proposed in literatures, but the timeliness of Monte Carlo method is very poor. It is proposed in the literature to use the probabilistic optimal power flow to study the location of energy storage, and 2 m point method is used, but the calculation accuracy of this method is not ideal. The application of Latin hypercube sampling method in power flow calculation is studied in this paper, and the superiority of Latin hypercube sampling method is illustrated by numerical examples.

Dabere na nyocha nke dị n’elu, akwụkwọ a na-eji usoro ịgba ọsọ ike nke puru omume na-amụ ihe kachasị mma nke nchekwa ike na usoro ike na nnukwu fotovoltaic ike. Nke mbu, ewebata ụdị nkesa nke puru omume yana usoro nlele Latin hypercube nke akụrụngwa na sistemu ike. Nke abuo, a na-eguzobe ụdị njikarịcha ihe dị iche iche na-atụle ọnụ ahịa nchekwa ike, ike na-agafe na njedebe na nkwụsị netwọk. N’ikpeazụ, a na-eme nyocha nyocha na IEEE24 node test system.

1. Ihe nleba anya ike na-erugharị

1.1 Ụdị ejighị n’aka nke components

Fotovoltaic, ibu na jenerato niile bụ mgbanwe na-enweghị usoro na-ejighị n’aka. Na ngụkọ nke probabilistic ike eruba nke netwọk nkesa, a kọwara ihe nlereanya nke puru omume na akwụkwọ. Site na nyocha nke data akụkọ ihe mere eme, ike mmepụta nke ike fotovoltaic na-esote nkesa BETA. Site n’itinye ikike nkesa ike ibu, a na-eche na ibu na-esote nkesa nkịtị, yana ọrụ nkesa nke puru omume ya bụ.

Foto (1)

Where, Pl is the load power; μ L and σ L are the expectation and variance of load respectively.

Ụdị ihe gbasara nke puru omume nke jenerato na-anabata nkesa ụzọ abụọ, yana ọrụ nkesa ihe gbasara nke puru omume bụ

(2)

Ebe, P bụ ihe gbasara nke puru omume nke ọrụ nkịtị nke generator; PG bụ ike mmepụta nke generator.

Mgbe ọkụ zuru oke n’ehihie, ike na-arụ ọrụ nke ebe nchekwa fotovoltaic dị ukwuu, na ike nke siri ike iji mee ihe n’oge ga-echekwa na batrị nchekwa ike. Mgbe ike ibu dị elu, batrị nchekwa ike ga-ahapụ ike echekwara. Nhazi nguzozi ike ozugbo nke sistemu nchekwa ike bụ

Mgbe ị na-akwụ ụgwọ

(3)

When the discharge

(4)

Mmachi

Foto,

Foto,

Picture, picture

Ebe, St bụ ike echekwara n’oge T; Pt bụ ụgwọ na mwepụ ike nke nchekwa ike; SL na SG bụ ike nke ịchaji na ịwụsa n’otu n’otu. η C na η D na-akwụ ụgwọ ma na-ebugharị arụmọrụ n’otu n’otu. Ds bụ ọnụ ọgụgụ mwepu nke onwe nke nchekwa ike.

1.2 Latin hypercube sampling method

Enwere usoro ịme anwansị, usoro dị nso na usoro nyocha nke enwere ike iji nyochaa ike sistemu n’okpuru ihe na-ejighị n’aka. ịme anwansị nke Monte Carlo bụ otu n’ime ụzọ kachasị zie ezie na algọridim nke ike na-erugharị, mana oge ya dị ala ma e jiri ya tụnyere nkenke dị elu. N’ihe banyere oge nlele dị ala, usoro a na-elegharakarị ọdụ nke ihe gbasara nkesa nke puru omume, ma iji mee ka ọ dịkwuo mma, ọ kwesịrị ịbawanye oge nlele. Usoro nlele Latin hypercube na-ezere nsogbu a. Ọ bụ usoro nlele ọkwa ọkwa, nke nwere ike hụ na isi ihe nlele gosipụtara nkesa nke puru omume nke ọma ma belata oge nlele nke ọma.

Figure 1 shows the expectation and variance of Latin hypercube sampling method and Monte Carlo simulation method with sampling times ranging from 10 to 200. The overall trend of results obtained by the two methods is decreasing. However, the expectation and variance obtained by monte Carlo method are very unstable, and the results obtained by multiple simulations are not the same with the same sampling times. The variance of Latin hypercube sampling method decreases steadily with the increase of sampling times, and the relative error decreases to less than 5% when the sampling times are more than 150. It is worth noting that the sampling point of the Latin hypercube sampling method is symmetric about the Y-axis, so its expected error is 0, which is also its advantage.

Foto ahụ

FIG. 1 Tụnyere oge nlele dị iche iche n’etiti MC na LHS

Usoro nleba anya hypercube Latin bụ usoro nlere anya. Site na imeziwanye usoro nlere anya nke ọgbọ ntinye nke mgbanwe na-enweghị usoro, uru nlele nwere ike gosipụta nke ọma n’ozuzu oke nke mgbanwe mgbanwe. A na-ekewa usoro nlele anya ụzọ abụọ.

(1) Nlereanya

Xi (I = 1, 2,…,m) bụ m mgbanwe mgbanwe, na oge nlele bụ N, dị ka egosiri na FIG. 2. A na-ekewa usoro nkesa nke puru omume nke Xi n’ime N etiti oge yana ohere nhata na enweghị ihe ọ bụla, a na-ahọrọ etiti etiti oge ọ bụla dị ka uru nlele nke ihe gbasara nke puru omume Y, na mgbe ahụ, uru nlele Xi = p-1 (Yi) bụ. gbakọọ site na iji ngbanwe ọrụ, na Xi gbakọrọ bụ ihe nlele uru nke random variable.

Foto ahụ

Onyonyo 2 schematic eserese nke LHS

(2) Permutations

A na-ahazi ụkpụrụ nlele nke mgbanwe ndị na-enweghị usoro enwetara site na (1), ya mere njikọ dị n’etiti m random variables bụ 1, nke enweghị ike ịgbakọ. Enwere ike ịnakwere usoro orthogonalization usoro gram-Schmidt iji belata njikọ dị n’etiti ụkpụrụ nlele nke mgbanwe mgbanwe na-enweghị usoro. Nke mbụ, a na-emepụta matriks nke usoro K × M I = [I1, I2…, IK] T. A na-ahazi ihe ndị dị n’ahịrị nke ọ bụla site na 1 ruo M, na ha na-anọchi anya ọnọdụ uru nlele nke mgbanwe mgbanwe mbụ.

Ntụgharị dị mma

Foto ahụ

Ntụgharị azụ

Foto ahụ

“Picture” represents assignment, takeout(Ik,Ij) represents calculation of residual value in linear regression Ik=a+bIj, rank(Ik) represents new vector formed by the sequence number of elements in orientation Ik from small to large.

Mgbe bidirectional iteration ruo mgbe RMS uru ρ, nke na-anọchi anya mmekọrịta, adịghị ibelata, matriks ọnọdụ nke ọ bụla random mgbanwe mgbe permutation nwetara, na mgbe ahụ, permutation matrix nke random variables na kacha nta mmekọrịta nwere ike nweta.

(5)

Ebe, foto a bụ ọnụọgụ mmekọrịta dị n’etiti Ik na Ij, cov bụ covariance, na VAR bụ ọdịiche.

2. Multi-ebumnuche njikarịcha nhazi nke ike nchekwa usoro

2.1 Ọrụ ebumnobi

Iji kwalite ike na ikike nke usoro nchekwa nchekwa ike, a na-arụ ọrụ kachasị mma nke ọtụtụ ihe na-atụle ego nke usoro nchekwa nchekwa ike, ikike nkwụsị ike na nkwụsị netwọk. N’ihi akụkụ dị iche iche nke ihe ngosi ọ bụla, a na-eme nhazi nhazi maka ihe ngosi ọ bụla. Mgbe nhazi nhazi nke ihie ụzọ, oke uru nke ụkpụrụ a na-ahụ anya nke mgbanwe dị iche iche ga-adị n’etiti (0,1), na data ahaziri bụ ọnụọgụ dị ọcha na-enweghị nkeji. N’ọnọdụ dị adị, enwere ike ịdị iche na ntinye aka na ihe ngosi ọ bụla. Ọ bụrụ na e nyere ihe nrịbama nke ọ bụla n’otu ibu, enwere ike nyochaa ma mụọ ihe nrịbama dị iche iche.

(6)

Where, w is the index to be optimized; Wmin and wmax are the minimum and maximum of the original function without standardization.

Ebumnuche ebumnuche bụ

(7)

In the formula, λ1 ~ λ3 are weight coefficients, Eloss, PE and CESS are standardized branch network loss, branch active power crossing probability and energy storage investment cost respectively.

2.2 Mkpụrụ ndụ algọridim

Mkpụrụ ndụ ihe nketa algọridim bụ ụdị njikarịcha algọridim emebere site n’iṅomi usoro mkpụrụ ndụ ihe nketa na iwu evolushọn nke ịlanarị nke kacha mma na nlanarị nke kacha mma na okike. Ọ na-ebu ụzọ mee koodu, mmalite ọnụ ọgụgụ onye ọ bụla na-edochi anya n’aha onye ọ bụla (ihe ngwọta nwere ike ime maka nsogbu ahụ), ya mere, ngwọta ọ bụla nwere ike ime bụ site na mgbanwe mgbanwe genotype phenotype, iji mee nhọrọ dịka iwu nke okike si dị maka onye ọ bụla, wee họrọ ya. ọgbọ ọ bụla na-esote ọgbọ nke Mgbakọ gburugburu ebe obibi imeghari ka ike onye, ​​ruo mgbe kasị adaptable na gburugburu ebe obibi nke onye, ​​Mgbe decoding, ọ bụ ihe kacha mma ngwọta nke nsogbu.

In this paper, the power system including photovoltaic and energy storage is firstly calculated by the probabilistic power flow algorithm, and the obtained data is used as the input variable of the genetic algorithm to solve the problem. The calculation process is shown in Figure 3, which is mainly divided into the following steps:

Foto ahụ

FIG. 3 Algorithm flow

(1) Input system, photovoltaic and energy storage data, and perform Latin hypercube sampling and Gram-Schmidt sequence orthogonalization;

(2) Tinye data egosipụtara n’ime usoro mgbako ọkụ ọkụ wee dekọọ nsonaazụ mgbako;

(3) chromosome kpuchiri nsonaazụ nsonaazụ ya iji mepụta ọnụọgụ mbụ dabara na uru nlele;

(4) Gbakọọ ịdị mma nke onye ọ bụla n’ime ọnụ ọgụgụ mmadụ;

(5) họrọ, gafee na mutate iji mepụta ọgbọ ọhụrụ nke ọnụ ọgụgụ mmadụ;

(6) Kpebie ma ihe ndị a chọrọ, ma ọ bụrụ na ọ bụghị, laghachi nzọụkwụ (4); Ọ bụrụ ee, a na-emepụta ihe ngwọta kachasị mma ma emechaa decod.

3. Ntụle ihe atụ

A na-eme ka usoro mgbaba ike nke puru omume ma nyochaa ya na IEEE24-node test system egosiri na FIG. 4, nke ọkwa voltaji nke 1-10 ọnụ bụ 138 kV, na nke 11-24 ọnụ bụ 230 kV.

Foto ahụ

Figure 4 IEEE24 node test system

3.1 Mmetụta nke ọdụ ọkụ fotovoltaic na sistemụ ọkụ

Ebe nchekwa ọkụ fotovoltaic na sistemụ ike, ọnọdụ na ikike nke usoro ike ga-emetụta voltaji node na ike alaka, ya mere, tupu nyocha nke mmetụta nke usoro nchekwa nchekwa ike maka grid ike, ngalaba a na-ebu ụzọ nyochaa mmetụta nke ike fotovoltaic. ọdụ na usoro, fotovoltaic nweta usoro na akwụkwọ a, omume nke njedebe nke ihe gbasara nke puru omume, nkwụsị netwọk na ihe ndị ọzọ emeela na nyocha simulation.

As can be seen from FIG. 5(a), after photovoltaic power station is connected, nodes with smaller branch power flow overlimit are as follows: 11, 12, 13, 23, 13 to balance the node node, the node voltage and the phase Angle is given, have the effect of stable power grid power balance, 11, 12 and 23 instead of directly connected, as a result, several nodes connected to the limit the probability of smaller and more power, photovoltaic power station will access the node with balance effect is less on the impact of power system.

Foto ahụ

Ọgụgụ 5. (a) nchikota nke ike na-agbapụ na-agaghị ekwe omume (b) mgbanwe voltaji node (c) ngụkọta usoro netwọkụ na-efunahụ ebe PV dị iche iche.

Na mgbakwunye na oke ike nke ike, akwụkwọ a na-enyochakwa mmetụta nke fotovoltaic na voltaji node, dị ka egosiri na FIG. 5 (b). Ọkọlọtọ deviations nke voltaji amplitudes nke ọnụ 1, 3, 8, 13, 14, 15 na 19 na-ahọrọ maka ntụnyere. N’ozuzu ya, njikọ nke ụlọ ọrụ fotovoltaic na ọkụ eletrik adịghị enwe mmetụta dị ukwuu na voltaji nke ọnụ ọgụgụ, ma ụlọ ọrụ fotovoltaic nwere mmetụta dị ukwuu na voltaji nke a-Nodes na oghere ha dị nso. Tụkwasị na nke ahụ, na usoro nke nakweere ihe atụ mgbako, site na ntụnyere, a na-achọpụta na ebe nchekwa fotovoltaic dị mma maka ịnweta ụdị ọnụ: ① nodes na ọkwa voltaji dị elu, dị ka 14, 15, 16, wdg. voltaji fọrọ nke nta ka ọ dịghị agbanwe; (2) ọnụ na-akwado ndị na-emepụta ọkụ ma ọ bụ igwefoto na-edozi, dị ka 1, 2, 7, wdg; (3) na nguzogide ahịrị dị ukwuu na njedebe nke ọnụ.

Iji nyochaa mmetụta nke ohere ịnweta PV na mkpokọta netwọkụ nke sistemu ike, akwụkwọ a na-eme ntụnyere dị ka egosiri na eserese 5 (c). Enwere ike ịhụ na ọ bụrụ na ụfọdụ ọnụ nwere nnukwu ibu ibu na enweghị ike ọkụ na-ejikọta na pv power station, a ga-ebelata nkwụsị netwọk nke usoro ahụ. N’ụzọ megidere nke ahụ, ọnụ 21, 22 na 23 bụ njedebe ọkụ ọkụ, nke na-ahụ maka nnyefe ike etiti. Igwe ọkụ fotovoltaic ejikọrọ na ọnụ ndị a ga-eme ka nnukwu mfu netwọkụ. Ya mere, a ga-ahọrọ ebe ịnweta ike pv na njedebe nke ike ma ọ bụ ọnụ na nnukwu ibu. Ụdị ohere a nwere ike ime ka ikesa ike nke usoro ahụ dịkwuo mma ma belata nkwụsị netwọk nke usoro ahụ.

Based on the three factors in the analysis of the above results, node 14 is taken as the access point of photovoltaic power station in this paper, and then the influence of the capacity of different photovoltaic power stations on the power system is studied.

Ihe osise 6 (a) na-enyocha mmetụta nke ikike fotovoltaic na usoro. Enwere ike ịhụ na ngbanwe ọkọlọtọ nke ike na-arụ ọrụ nke alaka ọ bụla na-abawanye na mmụba nke ikike fotovoltaic, na enwere mmekọrịta dị mma n’etiti abụọ ahụ. Ewezuga alaka dị iche iche nke egosiri na ọnụ ọgụgụ ahụ, ụkpụrụ ọkọlọtọ nke alaka ndị ọzọ na-erughị 5 ma na-egosi njikọ njikọ, nke a na-eleghara anya maka ịdị mma nke ịbịaru. Enwere ike ịhụ na njikọ fotovoltaic grid nwere mmetụta dị ukwuu na ike nke njikọ kpọmkwem na ebe ịnweta fotovoltaic ma ọ bụ alaka ndị dị n’akụkụ. N’ihi oke nnyefe nke nnyefe ike, eriri nnyefe nke ọnụ ọgụgụ nke ihe owuwu na itinye ego dị ukwuu, ya mere ịwụnye ọdụ ọkụ fotovoltaic, kwesịrị ịtụle njedebe nke ikike njem, họrọ mmetụta kachasị nta na ịnweta akara na ebe kachasị mma, na mgbakwunye, ịhọrọ ikike kacha mma nke ọdụ ọkụ fotovoltaic ga-ekere òkè dị mkpa iji belata mmetụta a.

Foto ahụ

Ọnụọgụ 6. (a) Alaka na-arụ ọrụ ike ọkọlọtọ ọkọlọtọ (b) alaka ike na-asọpụta na-enweghị ike ime (c) mkpokọta netwọk usoro mfu n’okpuru ike fotovoltaic dị iche iche.

FIG. 6 (b) tụlere ohere nke ike na-arụ ọrụ gafere oke ngalaba ọ bụla n’okpuru ikike ọdụ ụgbọ elu pv dị iche iche. Ewezuga alaka ndị e gosiri na ọnụ ọgụgụ a, alaka ndị ọzọ agafeghị oke ma ọ bụ ihe puru omume dị obere. Tụnyere FIG. 6(a), enwere ike ịhụ na ihe gbasara nke puru omume nke enweghị oke na ngbanwe ọkọlọtọ abụghị ihe jikọrọ ya. Ike na-arụ ọrụ nke eriri nwere nnukwu mgbanwe mgbanwe ọkọlọtọ apụtaghị na ọ bụ njedebe, ihe kpatara ya na-ejikọta ya na ntụgharị ntụgharị nke ike mmepụta fotovoltaic. Ọ bụrụ na ọ dị n’otu ụzọ ahụ dị ka isi ike nke alaka ụlọ ọrụ mbụ, obere ike fotovoltaic nwekwara ike ime ka njedebe. Mgbe ike pv dị nnọọ ukwuu, ike ọkụ nwere ike ọ gaghị agafe oke.

Na FIG. 6 (c), ngụkọta netwọkụ nke usoro ahụ na-abawanye na mmụba nke ikike fotovoltaic, ma mmetụta a abụghị ihe doro anya. Mgbe ikike fotovoltaic na-abawanye site na 60 MW, ngụkọta netwọkụ efu na-abawanye naanị site na 0.5%, ntụgharị 0.75 MW. Ya mere, mgbe ị na-etinye ọdụ ọkụ pv, a ga-ewere nkwụsị netwọk dị ka ihe nke abụọ, na ihe ndị na-enwe mmetụta dị ukwuu na arụ ọrụ siri ike nke usoro ahụ kwesịrị iburu ụzọ buru ụzọ tụlee, dị ka mgbanwe ọkụ nke eriri nnyefe na ohere na-enweghị njedebe. .

3.2 Mmetụta nke ịnweta nchekwa ike na usoro

Nkebi 3.1 ohere ịnweta na ikike nke ọdụ ọkụ fotovoltaic dabere na usoro ike